Today, huge amounts of data are being collected with spatial and temporalcomponents from sources such as meteorological, satellite imagery etc.Efficient visualisation as well as discovery of useful knowledge from thesedatasets is therefore very challenging and becoming a massive economic need.Data Mining has emerged as the technology to discover hidden knowledge in verylarge amounts of data. Furthermore, data mining techniques could be applied todecrease the large size of raw data by retrieving its useful knowledge asrepresentatives. As a consequence, instead of dealing with a large size of rawdata, we can use these representatives to visualise or to analyse withoutlosing important information. This paper presents a new approach based ondifferent clustering techniques for data reduction to help analyse very largespatio-temporal data. We also present and discuss preliminary results of thisapproach.
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